Grant Funding: NHLBI R21 – Sleep pathology and cardiac aging
Methodologies and algorithms from the pilot award informed the development and design of this funded grant application. Source: R21HL175584 (NIH RePORTER)
Grant Funding: U01: Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches
PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) is the most common form of dementia characterized by progressive loss of cognitive function. Unfortunately, currently there is no effective treatment for AD and clinical interventions of AD have largely failed despite enormous efforts. For the current application, we seek to develop multimodal machine learning models by leveraging the rich collection of AD-related omics data and phenotypical data recently generated from large-scale collaborative projects such as Alzheimer Disease Neuroimaging Initiative (ADNI), Accelerating Medicines Partnership-AD (AMP-AD) and the Alzheimer's Disease Sequencing Project (ADSP). Three aims will be pursued in the current application. Aim 1. We will build an expandable multimodal unsupervised machine learning framework to investigate AD heterogeneity. Given the multifactorial nature of AD, we will perform AD subtyping by harnessing the rich information across multiple spectrum of data. Aim 2.…
Grant Funding: R21 AG088872
Title: Characterizing autonomic impairments in Frontotemporal Dementia This R21 builds upon the tech ready cohort that was established by the pilot project funding. Public Health Relevance Statement: This proposal will test the accuracy and reliability of autonomic measurements in bvFTD patients. Measurements will be collected both with established equipment and via at-home devices to assess the validity of the latter. Finally, autonomic measurements will be correlated to socioemotional dysfunction in patients. Source: R21 AG088872 (NIH RePORTER)
Grant Funding: NIA R01 – Digital Cognitive Assessment of Preclinical Alzheimer’s Disease and Related Dementias
Analyses supported by the pilot project contributed to this awarded grant submission. Source: 1R01AG083735 (NIH RePORTER)
Oral Presentation: Alzheimer’s Association International Conference 2024 – Technology and Dementia Preconference
Archna Bhatia presented" Intelligent Cognitive Assistant leveraging natural language processing to provide word retrieval" at the a2 Collective Session at the Technology and Dementia Pre-conference.
Award: Fortune Recommends names Livindi the most comprehensive older adult monitoring system
Firsthand product testing testing and expert review have resulted in Fortune magazine recommending Livindi as the most comprehensive home monitoring system and recognition as one of the 6 best older adult monitoring systems. What they said: "Regarding elderly monitoring systems, none seem as detailed and comprehensive as Livindi. This system provides a tablet with closed captioning, sensors, a call button, and a concierge service. This elderly monitoring system can be customized to fit your loved one’s needs and even includes the option for wearable technology if you want coverage outside of the home. This system not only can track activity and report any changes, but it also offers fall detection and biometrics information (for a fee). Although this system has a higher upfront cost for all of its technological features, its monthly service fee is…
Grant Funding: R01 AG082354
Title: Genomics-guided sleep biomarker discovery for early Alzheimer's disease: A wearables study This R01 builds upon the technology and algorithms for sleep-based metrics developed in the MassAITC pilot project, utilizing the same EEG device. It shifts the study from a general AD-risk factor population to a genetic AD-risk factor population. Public Health Relevance Statement: Sleep-based metrics are promising as potential noninvasive biomarkers for the early identification of individuals at risk of Alzheimer’s disease. In this R01 Research Project Grant, we will collect and analyze sleep and activity data from three wearable devices for sleep, heart rate, and activity monitoring along with blood biomarkers for Alzheimer’s disease from elderly Mass General Brigham Biobank participants with elevated genetic susceptibility to Alzheimer’s disease. We will also develop artificial intelligence tools for digital phenotyping and the discovery of novel…